import tensorflow as tf
from tensorflow.models.rnn import rnn_cell, rnn
from tensorflow.models.rnn import seq2seq
from tensorflow.models.rnn.ptb import reader
import numpy as np


class cnnlstm_class(object):
   
    def __init__(
      self, embedding_mat, non_static, lstm_type, hidden_unit, sequence_length, max_pool_size,
      num_classes, embedding_size, filter_sizes, num_filters, l2_reg_lambda=0.0):

        # Placeholders for input, output and dropout
        self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name="input_x")
        self.input_y = tf.placeholder(tf.float32, [None, num_classes], name="input_y")
        self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")
        self.batch_size = tf.placeholder(tf.int32)
        self.pad = tf.placeholder(tf.float32, [None, 1, embedding_size, 1], name = "pad")
        self.real_len = tf.placeholder(tf.int32, [None], name = "real_len")

        # Keeping track of l2 regularization loss (optional)
        l2_loss = tf.constant(0.0)

        # Extend input to a 4D Tensor, because tf.nn.conv2d requires so.
        with tf.device('/cpu:0'), tf.name_scope("embedding"):
            if not non_static:
                W = tf.constant(embedding_mat, name = "W")
            else:
                W = tf.Variable(embedding_mat, name = "W")
            self.embedded_chars = tf.nn.embedding_lookup(W, self.input_x)
            emb = tf.expand_dims(self.embedded_chars, -1)        

        # CNN
        pooled_concat = []
        reduced = np.int32(np.ceil((sequence_length)*1.0/max_pool_size))
        for i, filter_size in enumerate(filter_sizes):
            with tf.name_scope("conv-maxpool-%s" % filter_size):

                # Zero paddings so that the convolution output have dimension batch x sequence_length x emb_size x channel
                num_prio = (filter_size-1) // 2
                num_post = (filter_size-1) - num_prio
                pad_prio = tf.concat(1, [self.pad] * num_prio)
                pad_post = tf.concat(1, [self.pad] * num_post)
                emb_pad = tf.concat(1, [pad_prio, emb, pad_post])
                
                # Convolution Layer
                filter_shape = [filter_size, embedding_size, 1, num_filters]
                W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
                b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")
                conv = tf.nn.conv2d(
                    emb_pad,
                    W,
                    strides=[1, 1, 1, 1],
                    padding="VALID",
                    name="conv")
                
                # Apply nonlinearity
                h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")

                # Maxpooling over the outputs
                pooled = tf.nn.max_pool(
                    h,
                    ksize=[1, max_pool_size, 1, 1],
                    strides=[1, max_pool_size, 1, 1],
                    padding='SAME',
                    name="pool")
                pooled = tf.reshape(pooled, [-1, reduced, num_filters])
                pooled_concat.append(pooled)

        pooled_concat = tf.concat(2, pooled_concat)
        pooled_concat = tf.nn.dropout(pooled_concat, self.dropout_keep_prob)

        # LSTM
        if lstm_type == "gru":
            lstm_cell = rnn_cell.GRUCell(num_units = hidden_unit, input_size = embedding_size)
        else: 
            if lstm_type == "basic":
                lstm_cell = rnn_cell.BasicLSTMCell(num_units = hidden_unit, input_size = embedding_size)
            else:
                lstm_cell = rnn_cell.LSTMCell(num_units = hidden_unit, input_size = embedding_size, use_peepholes = True)
        lstm_cell = rnn_cell.DropoutWrapper(lstm_cell, output_keep_prob = self.dropout_keep_prob)
        
        self._initial_state = lstm_cell.zero_state(self.batch_size, tf.float32)
        inputs = [tf.squeeze(input_, [1]) for input_ in tf.split(1, reduced, pooled_concat)]
        outputs, state = rnn.rnn(lstm_cell, inputs, initial_state=self._initial_state, sequence_length = self.real_len)
        
        # Collect the appropriate last words into variable output (dimension = batch x embedding_size)
        output = outputs[0]
        with tf.variable_scope("Output"):
            tf.get_variable_scope().reuse_variables()
            one = tf.ones([1, hidden_unit], tf.float32)
            for i in range(1,len(outputs)):
                ind = self.real_len < (i+1)
                ind = tf.to_float(ind)
                ind = tf.expand_dims(ind, -1)
                mat = tf.matmul(ind, one)
                output = tf.add(tf.mul(output, mat),tf.mul(outputs[i], 1.0 - mat))

        # Final (unnormalized) scores and predictions
        with tf.name_scope("output"):
            self.W = tf.Variable(tf.truncated_normal([hidden_unit, num_classes], stddev=0.1), name="W")
            b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b")
            l2_loss += tf.nn.l2_loss(W)
            l2_loss += tf.nn.l2_loss(b)
            self.scores = tf.nn.xw_plus_b(output, self.W, b, name="scores")
            self.predictions = tf.argmax(self.scores, 1, name="predictions")

        # CalculateMean cross-entropy loss
        with tf.name_scope("loss"):
            losses = tf.nn.softmax_cross_entropy_with_logits(self.scores, self.input_y)
            self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss

        # Accuracy
        with tf.name_scope("accuracy"):
            correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
            self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy")
